Overview

Brought to you by YData

Dataset statistics

Number of variables23
Number of observations24532
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.3 MiB
Average record size in memory184.0 B

Variable types

Text3
Numeric17
DateTime1
Categorical2

Alerts

acousticness is highly overall correlated with energyHigh correlation
duration_min is highly overall correlated with duration_ms and 1 other fieldsHigh correlation
duration_ms is highly overall correlated with duration_min and 1 other fieldsHigh correlation
duration_sec is highly overall correlated with duration_min and 1 other fieldsHigh correlation
energy is highly overall correlated with acousticness and 1 other fieldsHigh correlation
key is highly overall correlated with key_nameHigh correlation
key_name is highly overall correlated with keyHigh correlation
loudness is highly overall correlated with energyHigh correlation
track_popularity has 2081 (8.5%) zerosZeros
key has 2604 (10.6%) zerosZeros
instrumentalness has 8918 (36.4%) zerosZeros

Reproduction

Analysis started2025-12-02 22:39:01.069670
Analysis finished2025-12-02 22:39:32.410120
Duration31.34 seconds
Software versionydata-profiling vv4.17.0
Download configurationconfig.json

Variables

Distinct21757
Distinct (%)88.7%
Missing0
Missing (%)0.0%
Memory size191.8 KiB
2025-12-02T14:39:32.670880image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length144
Median length91
Mean length16.078469
Min length1

Characters and Unicode

Total characters394437
Distinct characters413
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique20119 ?
Unique (%)82.0%

Sample

1st rowDance Monkey
2nd rowROXANNE
3rd rowBlinding Lights
4th rowCircles
5th rowTusa
ValueCountFrequency (%)
3274
 
4.3%
the1983
 
2.6%
feat1829
 
2.4%
you1316
 
1.7%
me1173
 
1.5%
i844
 
1.1%
love797
 
1.0%
a633
 
0.8%
in625
 
0.8%
to613
 
0.8%
Other values (14466)62901
82.8%
2025-12-02T14:39:33.125253image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
51456
 
13.0%
e35638
 
9.0%
a24683
 
6.3%
o23927
 
6.1%
i20351
 
5.2%
t18586
 
4.7%
n18172
 
4.6%
r16251
 
4.1%
l12269
 
3.1%
s12025
 
3.0%
Other values (403)161079
40.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)394437
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
51456
 
13.0%
e35638
 
9.0%
a24683
 
6.3%
o23927
 
6.1%
i20351
 
5.2%
t18586
 
4.7%
n18172
 
4.6%
r16251
 
4.1%
l12269
 
3.1%
s12025
 
3.0%
Other values (403)161079
40.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)394437
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
51456
 
13.0%
e35638
 
9.0%
a24683
 
6.3%
o23927
 
6.1%
i20351
 
5.2%
t18586
 
4.7%
n18172
 
4.6%
r16251
 
4.1%
l12269
 
3.1%
s12025
 
3.0%
Other values (403)161079
40.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)394437
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
51456
 
13.0%
e35638
 
9.0%
a24683
 
6.3%
o23927
 
6.1%
i20351
 
5.2%
t18586
 
4.7%
n18172
 
4.6%
r16251
 
4.1%
l12269
 
3.1%
s12025
 
3.0%
Other values (403)161079
40.8%
Distinct10314
Distinct (%)42.0%
Missing0
Missing (%)0.0%
Memory size191.8 KiB
2025-12-02T14:39:33.439033image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length69
Median length40
Mean length10.075452
Min length2

Characters and Unicode

Total characters247171
Distinct characters215
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6410 ?
Unique (%)26.1%

Sample

1st rowTones and I
2nd rowArizona Zervas
3rd rowThe Weeknd
4th rowPost Malone
5th rowKAROL G
ValueCountFrequency (%)
the1307
 
3.0%
554
 
1.3%
dj163
 
0.4%
lil141
 
0.3%
of132
 
0.3%
mike130
 
0.3%
john122
 
0.3%
queen120
 
0.3%
j120
 
0.3%
martin107
 
0.2%
Other values (11242)40531
93.3%
2025-12-02T14:39:33.950615image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e21552
 
8.7%
a19702
 
8.0%
18895
 
7.6%
i14377
 
5.8%
o14103
 
5.7%
n13803
 
5.6%
r12363
 
5.0%
l10386
 
4.2%
s9080
 
3.7%
t8440
 
3.4%
Other values (205)104470
42.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)247171
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e21552
 
8.7%
a19702
 
8.0%
18895
 
7.6%
i14377
 
5.8%
o14103
 
5.7%
n13803
 
5.6%
r12363
 
5.0%
l10386
 
4.2%
s9080
 
3.7%
t8440
 
3.4%
Other values (205)104470
42.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)247171
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e21552
 
8.7%
a19702
 
8.0%
18895
 
7.6%
i14377
 
5.8%
o14103
 
5.7%
n13803
 
5.6%
r12363
 
5.0%
l10386
 
4.2%
s9080
 
3.7%
t8440
 
3.4%
Other values (205)104470
42.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)247171
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e21552
 
8.7%
a19702
 
8.0%
18895
 
7.6%
i14377
 
5.8%
o14103
 
5.7%
n13803
 
5.6%
r12363
 
5.0%
l10386
 
4.2%
s9080
 
3.7%
t8440
 
3.4%
Other values (205)104470
42.3%

track_popularity
Real number (ℝ)

Zeros 

Distinct101
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40.727458
Minimum0
Maximum100
Zeros2081
Zeros (%)8.5%
Negative0
Negative (%)0.0%
Memory size191.8 KiB
2025-12-02T14:39:34.092313image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q125
median44
Q359
95-th percentile75
Maximum100
Range100
Interquartile range (IQR)34

Descriptive statistics

Standard deviation23.246349
Coefficient of variation (CV)0.57077828
Kurtosis-0.86374669
Mean40.727458
Median Absolute Deviation (MAD)16
Skewness-0.29749653
Sum999126
Variance540.39272
MonotonicityDecreasing
2025-12-02T14:39:34.217320image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
02081
 
8.5%
57439
 
1.8%
45429
 
1.7%
54426
 
1.7%
50426
 
1.7%
51423
 
1.7%
52420
 
1.7%
49414
 
1.7%
40414
 
1.7%
55413
 
1.7%
Other values (91)18647
76.0%
ValueCountFrequency (%)
02081
8.5%
1397
 
1.6%
2253
 
1.0%
3203
 
0.8%
4177
 
0.7%
5159
 
0.6%
6145
 
0.6%
7120
 
0.5%
8147
 
0.6%
9136
 
0.6%
ValueCountFrequency (%)
1001
 
< 0.1%
991
 
< 0.1%
985
< 0.1%
973
 
< 0.1%
961
 
< 0.1%
952
 
< 0.1%
945
< 0.1%
937
< 0.1%
924
< 0.1%
919
< 0.1%
Distinct17569
Distinct (%)71.6%
Missing0
Missing (%)0.0%
Memory size191.8 KiB
2025-12-02T14:39:34.504239image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length151
Median length96
Mean length16.545288
Min length1

Characters and Unicode

Total characters405889
Distinct characters342
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique14278 ?
Unique (%)58.2%

Sample

1st rowDance Monkey (Stripped Back) / Dance Monkey
2nd rowROXANNE
3rd rowBlinding Lights
4th rowHollywood's Bleeding
5th rowTusa
ValueCountFrequency (%)
the3309
 
4.5%
1329
 
1.8%
of1170
 
1.6%
feat829
 
1.1%
you734
 
1.0%
a642
 
0.9%
me637
 
0.9%
deluxe625
 
0.9%
to583
 
0.8%
edition534
 
0.7%
Other values (13439)62488
85.7%
2025-12-02T14:39:34.926972image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
48348
 
11.9%
e37887
 
9.3%
o23816
 
5.9%
a23193
 
5.7%
i20962
 
5.2%
t19120
 
4.7%
n18764
 
4.6%
r17789
 
4.4%
s15049
 
3.7%
l13865
 
3.4%
Other values (332)167096
41.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)405889
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
48348
 
11.9%
e37887
 
9.3%
o23816
 
5.9%
a23193
 
5.7%
i20962
 
5.2%
t19120
 
4.7%
n18764
 
4.6%
r17789
 
4.4%
s15049
 
3.7%
l13865
 
3.4%
Other values (332)167096
41.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)405889
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
48348
 
11.9%
e37887
 
9.3%
o23816
 
5.9%
a23193
 
5.7%
i20962
 
5.2%
t19120
 
4.7%
n18764
 
4.6%
r17789
 
4.4%
s15049
 
3.7%
l13865
 
3.4%
Other values (332)167096
41.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)405889
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
48348
 
11.9%
e37887
 
9.3%
o23816
 
5.9%
a23193
 
5.7%
i20962
 
5.2%
t19120
 
4.7%
n18764
 
4.6%
r17789
 
4.4%
s15049
 
3.7%
l13865
 
3.4%
Other values (332)167096
41.2%
Distinct5344
Distinct (%)21.8%
Missing0
Missing (%)0.0%
Memory size191.8 KiB
Minimum1957-01-01 00:00:00
Maximum2020-01-29 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-12-02T14:39:35.042668image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:35.177289image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

danceability
Real number (ℝ)

Distinct819
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.65211231
Minimum0
Maximum0.981
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size191.8 KiB
2025-12-02T14:39:35.313629image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.385
Q10.559
median0.668
Q30.76
95-th percentile0.869
Maximum0.981
Range0.981
Interquartile range (IQR)0.201

Descriptive statistics

Standard deviation0.14716224
Coefficient of variation (CV)0.22567009
Kurtosis-0.0041499447
Mean0.65211231
Median Absolute Deviation (MAD)0.099
Skewness-0.49735008
Sum15997.619
Variance0.021656726
MonotonicityNot monotonic
2025-12-02T14:39:35.443094image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.6990
 
0.4%
0.69487
 
0.4%
0.71586
 
0.4%
0.70886
 
0.4%
0.783
 
0.3%
0.69982
 
0.3%
0.68882
 
0.3%
0.73480
 
0.3%
0.68379
 
0.3%
0.73377
 
0.3%
Other values (809)23700
96.6%
ValueCountFrequency (%)
01
< 0.1%
0.07711
< 0.1%
0.07871
< 0.1%
0.09851
< 0.1%
0.1161
< 0.1%
0.1181
< 0.1%
0.131
< 0.1%
0.1352
< 0.1%
0.142
< 0.1%
0.1411
< 0.1%
ValueCountFrequency (%)
0.9811
 
< 0.1%
0.9792
< 0.1%
0.9781
 
< 0.1%
0.9771
 
< 0.1%
0.9752
< 0.1%
0.9743
< 0.1%
0.9731
 
< 0.1%
0.9721
 
< 0.1%
0.9712
< 0.1%
0.973
< 0.1%

energy
Real number (ℝ)

High correlation 

Distinct951
Distinct (%)3.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.69120014
Minimum0.000175
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size191.8 KiB
2025-12-02T14:39:35.567220image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.000175
5-th percentile0.34755
Q10.569
median0.714
Q30.839
95-th percentile0.95
Maximum1
Range0.999825
Interquartile range (IQR)0.27

Descriptive statistics

Standard deviation0.1866416
Coefficient of variation (CV)0.27002541
Kurtosis-0.078027366
Mean0.69120014
Median Absolute Deviation (MAD)0.133
Skewness-0.61024469
Sum16956.522
Variance0.034835087
MonotonicityNot monotonic
2025-12-02T14:39:35.690806image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.83371
 
0.3%
0.82868
 
0.3%
0.72667
 
0.3%
0.71165
 
0.3%
0.87365
 
0.3%
0.68764
 
0.3%
0.78764
 
0.3%
0.75864
 
0.3%
0.7663
 
0.3%
0.71561
 
0.2%
Other values (941)23880
97.3%
ValueCountFrequency (%)
0.0001751
< 0.1%
0.008141
< 0.1%
0.01181
< 0.1%
0.01611
< 0.1%
0.01671
< 0.1%
0.02971
< 0.1%
0.03231
< 0.1%
0.0361
< 0.1%
0.03751
< 0.1%
0.03831
< 0.1%
ValueCountFrequency (%)
13
 
< 0.1%
0.9996
 
< 0.1%
0.9985
 
< 0.1%
0.9974
 
< 0.1%
0.9969
 
< 0.1%
0.9957
 
< 0.1%
0.9949
 
< 0.1%
0.99324
0.1%
0.99214
0.1%
0.99119
0.1%

key
Real number (ℝ)

High correlation  Zeros 

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.3726154
Minimum0
Maximum11
Zeros2604
Zeros (%)10.6%
Negative0
Negative (%)0.0%
Memory size191.8 KiB
2025-12-02T14:39:35.789985image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median6
Q39
95-th percentile11
Maximum11
Range11
Interquartile range (IQR)7

Descriptive statistics

Standard deviation3.6096413
Coefficient of variation (CV)0.67185925
Kurtosis-1.3089993
Mean5.3726154
Median Absolute Deviation (MAD)3
Skewness-0.025661163
Sum131801
Variance13.029511
MonotonicityNot monotonic
2025-12-02T14:39:35.883317image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
12938
12.0%
02604
10.6%
72542
10.4%
92301
9.4%
112191
8.9%
22142
8.7%
51977
8.1%
61955
8.0%
81783
7.3%
101733
7.1%
Other values (2)2366
9.6%
ValueCountFrequency (%)
02604
10.6%
12938
12.0%
22142
8.7%
3698
 
2.8%
41668
6.8%
51977
8.1%
61955
8.0%
72542
10.4%
81783
7.3%
92301
9.4%
ValueCountFrequency (%)
112191
8.9%
101733
7.1%
92301
9.4%
81783
7.3%
72542
10.4%
61955
8.0%
51977
8.1%
41668
6.8%
3698
 
2.8%
22142
8.7%

loudness
Real number (ℝ)

High correlation 

Distinct9873
Distinct (%)40.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-6.9261183
Minimum-46.448
Maximum1.275
Zeros0
Zeros (%)0.0%
Negative24527
Negative (%)> 99.9%
Memory size191.8 KiB
2025-12-02T14:39:35.991339image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-46.448
5-th percentile-12.749
Q1-8.44725
median-6.356
Q3-4.781
95-th percentile-3.035
Maximum1.275
Range47.723
Interquartile range (IQR)3.66625

Descriptive statistics

Standard deviation3.0810017
Coefficient of variation (CV)-0.44483816
Kurtosis4.490284
Mean-6.9261183
Median Absolute Deviation (MAD)1.765
Skewness-1.349001
Sum-169911.54
Variance9.4925718
MonotonicityNot monotonic
2025-12-02T14:39:36.109503image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-5.82413
 
0.1%
-5.13112
 
< 0.1%
-4.97312
 
< 0.1%
-5.57611
 
< 0.1%
-5.79911
 
< 0.1%
-5.00810
 
< 0.1%
-4.44310
 
< 0.1%
-6.18910
 
< 0.1%
-6.95910
 
< 0.1%
-5.64410
 
< 0.1%
Other values (9863)24423
99.6%
ValueCountFrequency (%)
-46.4481
< 0.1%
-36.6241
< 0.1%
-36.5091
< 0.1%
-35.961
< 0.1%
-35.4271
< 0.1%
-29.5611
< 0.1%
-28.3091
< 0.1%
-26.2791
< 0.1%
-26.2071
< 0.1%
-26.0871
< 0.1%
ValueCountFrequency (%)
1.2751
< 0.1%
0.6421
< 0.1%
0.5511
< 0.1%
0.3261
< 0.1%
0.3021
< 0.1%
-0.0461
< 0.1%
-0.0731
< 0.1%
-0.1551
< 0.1%
-0.1581
< 0.1%
-0.2471
< 0.1%

mode
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size191.8 KiB
1
13943 
0
10589 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters24532
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
113943
56.8%
010589
43.2%

Length

2025-12-02T14:39:36.216094image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-02T14:39:36.292453image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
113943
56.8%
010589
43.2%

Most occurring characters

ValueCountFrequency (%)
113943
56.8%
010589
43.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)24532
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
113943
56.8%
010589
43.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)24532
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
113943
56.8%
010589
43.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)24532
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
113943
56.8%
010589
43.2%

speechiness
Real number (ℝ)

Distinct1265
Distinct (%)5.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1096703
Minimum0
Maximum0.918
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size191.8 KiB
2025-12-02T14:39:36.386213image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.0296
Q10.0407
median0.063
Q30.137
95-th percentile0.341
Maximum0.918
Range0.918
Interquartile range (IQR)0.0963

Descriptive statistics

Standard deviation0.1046128
Coefficient of variation (CV)0.95388452
Kurtosis3.949097
Mean0.1096703
Median Absolute Deviation (MAD)0.0284
Skewness1.915425
Sum2690.4317
Variance0.010943837
MonotonicityNot monotonic
2025-12-02T14:39:36.508394image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.10377
 
0.3%
0.10275
 
0.3%
0.035473
 
0.3%
0.10768
 
0.3%
0.10968
 
0.3%
0.12365
 
0.3%
0.11264
 
0.3%
0.10163
 
0.3%
0.10661
 
0.2%
0.034660
 
0.2%
Other values (1255)23858
97.3%
ValueCountFrequency (%)
01
 
< 0.1%
0.02242
 
< 0.1%
0.02251
 
< 0.1%
0.02283
< 0.1%
0.0231
 
< 0.1%
0.02311
 
< 0.1%
0.02324
< 0.1%
0.02331
 
< 0.1%
0.02343
< 0.1%
0.02355
< 0.1%
ValueCountFrequency (%)
0.9181
< 0.1%
0.8771
< 0.1%
0.8651
< 0.1%
0.861
< 0.1%
0.8561
< 0.1%
0.8531
< 0.1%
0.8171
< 0.1%
0.7921
< 0.1%
0.781
< 0.1%
0.7771
< 0.1%

acousticness
Real number (ℝ)

High correlation 

Distinct3658
Distinct (%)14.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.18618964
Minimum0
Maximum0.994
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size191.8 KiB
2025-12-02T14:39:36.629623image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.0005481
Q10.0152
median0.0862
Q30.279
95-th percentile0.714
Maximum0.994
Range0.994
Interquartile range (IQR)0.2638

Descriptive statistics

Standard deviation0.22977056
Coefficient of variation (CV)1.2340674
Kurtosis1.4558035
Mean0.18618964
Median Absolute Deviation (MAD)0.08231
Skewness1.4986035
Sum4567.6042
Variance0.052794512
MonotonicityNot monotonic
2025-12-02T14:39:36.753220image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.10260
 
0.2%
0.11455
 
0.2%
0.10153
 
0.2%
0.10751
 
0.2%
0.12850
 
0.2%
0.10550
 
0.2%
0.12250
 
0.2%
0.12149
 
0.2%
0.14149
 
0.2%
0.11549
 
0.2%
Other values (3648)24016
97.9%
ValueCountFrequency (%)
01
< 0.1%
1.4 × 10-61
< 0.1%
1.44 × 10-61
< 0.1%
1.47 × 10-61
< 0.1%
1.66 × 10-61
< 0.1%
2.16 × 10-61
< 0.1%
2.22 × 10-61
< 0.1%
2.32 × 10-61
< 0.1%
2.43 × 10-61
< 0.1%
2.46 × 10-61
< 0.1%
ValueCountFrequency (%)
0.9941
 
< 0.1%
0.9921
 
< 0.1%
0.9893
< 0.1%
0.9862
< 0.1%
0.9852
< 0.1%
0.9842
< 0.1%
0.9833
< 0.1%
0.9821
 
< 0.1%
0.9793
< 0.1%
0.9783
< 0.1%

instrumentalness
Real number (ℝ)

Zeros 

Distinct4582
Distinct (%)18.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.094947066
Minimum0
Maximum0.994
Zeros8918
Zeros (%)36.4%
Negative0
Negative (%)0.0%
Memory size191.8 KiB
2025-12-02T14:39:36.875921image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1.97 × 10-5
Q30.006875
95-th percentile0.807
Maximum0.994
Range0.994
Interquartile range (IQR)0.006875

Descriptive statistics

Standard deviation0.23825902
Coefficient of variation (CV)2.5093879
Kurtosis5.0507348
Mean0.094947066
Median Absolute Deviation (MAD)1.97 × 10-5
Skewness2.548275
Sum2329.2414
Variance0.056767361
MonotonicityNot monotonic
2025-12-02T14:39:36.995923image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
08918
36.4%
1.05 × 10-519
 
0.1%
0.8719
 
0.1%
0.0010619
 
0.1%
0.0011918
 
0.1%
0.91617
 
0.1%
1.17 × 10-517
 
0.1%
0.90117
 
0.1%
0.12417
 
0.1%
0.001317
 
0.1%
Other values (4572)15454
63.0%
ValueCountFrequency (%)
08918
36.4%
1 × 10-63
 
< 0.1%
1.01 × 10-612
 
< 0.1%
1.02 × 10-67
 
< 0.1%
1.03 × 10-67
 
< 0.1%
1.04 × 10-617
 
0.1%
1.05 × 10-67
 
< 0.1%
1.06 × 10-67
 
< 0.1%
1.07 × 10-611
 
< 0.1%
1.08 × 10-611
 
< 0.1%
ValueCountFrequency (%)
0.9942
< 0.1%
0.9831
 
< 0.1%
0.9821
 
< 0.1%
0.9811
 
< 0.1%
0.9791
 
< 0.1%
0.9742
< 0.1%
0.9722
< 0.1%
0.9712
< 0.1%
0.971
 
< 0.1%
0.9693
< 0.1%

liveness
Real number (ℝ)

Distinct1605
Distinct (%)6.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.19114245
Minimum0
Maximum0.996
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size191.8 KiB
2025-12-02T14:39:37.119530image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.0563
Q10.0933
median0.127
Q30.247
95-th percentile0.521
Maximum0.996
Range0.996
Interquartile range (IQR)0.1537

Descriptive statistics

Standard deviation0.15669292
Coefficient of variation (CV)0.8197704
Kurtosis5.2467483
Mean0.19114245
Median Absolute Deviation (MAD)0.049
Skewness2.1192357
Sum4689.1066
Variance0.024552672
MonotonicityNot monotonic
2025-12-02T14:39:37.247206image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.111266
 
1.1%
0.108241
 
1.0%
0.11237
 
1.0%
0.109223
 
0.9%
0.104219
 
0.9%
0.107219
 
0.9%
0.105217
 
0.9%
0.106213
 
0.9%
0.112213
 
0.9%
0.101209
 
0.9%
Other values (1595)22275
90.8%
ValueCountFrequency (%)
01
< 0.1%
0.009461
< 0.1%
0.01311
< 0.1%
0.0151
< 0.1%
0.01551
< 0.1%
0.01581
< 0.1%
0.01631
< 0.1%
0.01651
< 0.1%
0.01671
< 0.1%
0.01681
< 0.1%
ValueCountFrequency (%)
0.9961
 
< 0.1%
0.9941
 
< 0.1%
0.9921
 
< 0.1%
0.9912
 
< 0.1%
0.993
< 0.1%
0.9885
< 0.1%
0.9853
< 0.1%
0.9841
 
< 0.1%
0.9832
 
< 0.1%
0.9821
 
< 0.1%

valence
Real number (ℝ)

Distinct1335
Distinct (%)5.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.50970813
Minimum0
Maximum0.99
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size191.8 KiB
2025-12-02T14:39:37.370397image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.129
Q10.327
median0.512
Q30.694
95-th percentile0.89645
Maximum0.99
Range0.99
Interquartile range (IQR)0.367

Descriptive statistics

Standard deviation0.23497211
Coefficient of variation (CV)0.46099345
Kurtosis-0.91019318
Mean0.50970813
Median Absolute Deviation (MAD)0.183
Skewness-9.7471934 × 10-5
Sum12504.16
Variance0.055211892
MonotonicityNot monotonic
2025-12-02T14:39:37.499085image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.96155
 
0.2%
0.38950
 
0.2%
0.34748
 
0.2%
0.56247
 
0.2%
0.53447
 
0.2%
0.3546
 
0.2%
0.53145
 
0.2%
0.50445
 
0.2%
0.3345
 
0.2%
0.39245
 
0.2%
Other values (1325)24059
98.1%
ValueCountFrequency (%)
01
 
< 0.1%
1 × 10-55
< 0.1%
0.01161
 
< 0.1%
0.01221
 
< 0.1%
0.01391
 
< 0.1%
0.01591
 
< 0.1%
0.02231
 
< 0.1%
0.02341
 
< 0.1%
0.02691
 
< 0.1%
0.02761
 
< 0.1%
ValueCountFrequency (%)
0.991
 
< 0.1%
0.9851
 
< 0.1%
0.9841
 
< 0.1%
0.9831
 
< 0.1%
0.9812
 
< 0.1%
0.981
 
< 0.1%
0.9792
 
< 0.1%
0.9781
 
< 0.1%
0.9775
< 0.1%
0.9763
< 0.1%

tempo
Real number (ℝ)

Distinct16328
Distinct (%)66.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean120.88731
Minimum0
Maximum239.44
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size191.8 KiB
2025-12-02T14:39:37.621301image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile80.2372
Q199.07575
median121.947
Q3135.00225
95-th percentile173.92945
Maximum239.44
Range239.44
Interquartile range (IQR)35.9265

Descriptive statistics

Standard deviation27.370983
Coefficient of variation (CV)0.22641735
Kurtosis-0.013948881
Mean120.88731
Median Absolute Deviation (MAD)18.855
Skewness0.4953468
Sum2965607.4
Variance749.17073
MonotonicityNot monotonic
2025-12-02T14:39:37.746466image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
127.99233
 
0.1%
127.99325
 
0.1%
127.99722
 
0.1%
128.00522
 
0.1%
127.99422
 
0.1%
127.98321
 
0.1%
128.00121
 
0.1%
127.99121
 
0.1%
128.01321
 
0.1%
128.01420
 
0.1%
Other values (16318)24304
99.1%
ValueCountFrequency (%)
01
< 0.1%
35.4771
< 0.1%
37.1141
< 0.1%
38.9851
< 0.1%
46.1691
< 0.1%
48.7181
< 0.1%
49.5971
< 0.1%
52.0171
< 0.1%
52.5381
< 0.1%
52.6541
< 0.1%
ValueCountFrequency (%)
239.441
< 0.1%
220.2521
< 0.1%
219.9911
< 0.1%
219.9611
< 0.1%
214.5161
< 0.1%
214.0471
< 0.1%
214.0171
< 0.1%
212.0581
< 0.1%
211.6441
< 0.1%
211.3691
< 0.1%

duration_ms
Real number (ℝ)

High correlation 

Distinct18175
Distinct (%)74.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean224614.92
Minimum4000
Maximum517125
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size191.8 KiB
2025-12-02T14:39:37.868003image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum4000
5-th percentile144383.85
Q1186335.25
median215440
Q3252865.5
95-th percentile336753.1
Maximum517125
Range513125
Interquartile range (IQR)66530.25

Descriptive statistics

Standard deviation60658.393
Coefficient of variation (CV)0.27005505
Kurtosis2.5772989
Mean224614.92
Median Absolute Deviation (MAD)32294.5
Skewness1.0965743
Sum5.5102532 × 109
Variance3.6794406 × 109
MonotonicityNot monotonic
2025-12-02T14:39:37.993644image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24000030
 
0.1%
19200029
 
0.1%
21000028
 
0.1%
16000020
 
0.1%
18000019
 
0.1%
19500018
 
0.1%
21600016
 
0.1%
22500016
 
0.1%
16800015
 
0.1%
15000015
 
0.1%
Other values (18165)24326
99.2%
ValueCountFrequency (%)
40001
< 0.1%
294931
< 0.1%
314291
< 0.1%
318751
< 0.1%
337502
< 0.1%
339001
< 0.1%
345601
< 0.1%
375001
< 0.1%
376401
< 0.1%
412501
< 0.1%
ValueCountFrequency (%)
5171251
< 0.1%
5168931
< 0.1%
5167601
< 0.1%
5159601
< 0.1%
5157031
< 0.1%
5156801
< 0.1%
5122801
< 0.1%
5114001
< 0.1%
5109331
< 0.1%
5103201
< 0.1%

release_year
Real number (ℝ)

Distinct63
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2011.103
Minimum1957
Maximum2020
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size191.8 KiB
2025-12-02T14:39:38.111470image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1957
5-th percentile1985
Q12008
median2016
Q32019
95-th percentile2019
Maximum2020
Range63
Interquartile range (IQR)11

Descriptive statistics

Standard deviation11.327624
Coefficient of variation (CV)0.0056325428
Kurtosis2.8761425
Mean2011.103
Median Absolute Deviation (MAD)3
Skewness-1.8442291
Sum49336379
Variance128.31506
MonotonicityNot monotonic
2025-12-02T14:39:38.233690image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20196706
27.3%
20182556
 
10.4%
20171845
 
7.5%
20161502
 
6.1%
20151260
 
5.1%
20141085
 
4.4%
2013700
 
2.9%
2020587
 
2.4%
2012587
 
2.4%
2008493
 
2.0%
Other values (53)7211
29.4%
ValueCountFrequency (%)
19572
 
< 0.1%
19581
 
< 0.1%
19603
 
< 0.1%
19611
 
< 0.1%
19621
 
< 0.1%
19635
 
< 0.1%
19646
 
< 0.1%
196510
 
< 0.1%
196612
< 0.1%
196728
0.1%
ValueCountFrequency (%)
2020587
 
2.4%
20196706
27.3%
20182556
 
10.4%
20171845
 
7.5%
20161502
 
6.1%
20151260
 
5.1%
20141085
 
4.4%
2013700
 
2.9%
2012587
 
2.4%
2011475
 
1.9%

release_month
Real number (ℝ)

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.4713028
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size191.8 KiB
2025-12-02T14:39:38.333986image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median7
Q310
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)7

Descriptive statistics

Standard deviation3.7086354
Coefficient of variation (CV)0.57308946
Kurtosis-1.3392325
Mean6.4713028
Median Absolute Deviation (MAD)3
Skewness-0.1036849
Sum158754
Variance13.753976
MonotonicityNot monotonic
2025-12-02T14:39:38.423354image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
13893
15.9%
112599
10.6%
102427
9.9%
92064
8.4%
121967
8.0%
61837
7.5%
81832
7.5%
51774
7.2%
31682
6.9%
71644
6.7%
Other values (2)2813
11.5%
ValueCountFrequency (%)
13893
15.9%
21266
 
5.2%
31682
6.9%
41547
 
6.3%
51774
7.2%
61837
7.5%
71644
6.7%
81832
7.5%
92064
8.4%
102427
9.9%
ValueCountFrequency (%)
121967
8.0%
112599
10.6%
102427
9.9%
92064
8.4%
81832
7.5%
71644
6.7%
61837
7.5%
51774
7.2%
41547
6.3%
31682
6.9%

release_day
Real number (ℝ)

Distinct31
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.347994
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size191.8 KiB
2025-12-02T14:39:38.525759image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q16
median14
Q322
95-th percentile29
Maximum31
Range30
Interquartile range (IQR)16

Descriptive statistics

Standard deviation9.3478434
Coefficient of variation (CV)0.65150871
Kurtosis-1.2536232
Mean14.347994
Median Absolute Deviation (MAD)8
Skewness0.071839847
Sum351985
Variance87.382176
MonotonicityNot monotonic
2025-12-02T14:39:38.630694image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
13166
 
12.9%
20879
 
3.6%
13852
 
3.5%
10845
 
3.4%
15826
 
3.4%
25823
 
3.4%
27811
 
3.3%
21791
 
3.2%
8791
 
3.2%
17780
 
3.2%
Other values (21)13968
56.9%
ValueCountFrequency (%)
13166
12.9%
2610
 
2.5%
3716
 
2.9%
4673
 
2.7%
5667
 
2.7%
6772
 
3.1%
7628
 
2.6%
8791
 
3.2%
9710
 
2.9%
10845
 
3.4%
ValueCountFrequency (%)
31423
1.7%
30580
2.4%
29612
2.5%
28698
2.8%
27811
3.3%
26735
3.0%
25823
3.4%
24702
2.9%
23637
2.6%
22757
3.1%

key_name
Categorical

High correlation 

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size191.8 KiB
C#/Db
2938 
C
2604 
G
2542 
A
2301 
B
2191 
Other values (7)
11956 

Length

Max length5
Median length1
Mean length2.4849177
Min length1

Characters and Unicode

Total characters60960
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowF#/Gb
2nd rowF#/Gb
3rd rowC#/Db
4th rowC
5th rowD

Common Values

ValueCountFrequency (%)
C#/Db2938
12.0%
C2604
10.6%
G2542
10.4%
A2301
9.4%
B2191
8.9%
D2142
8.7%
F1977
8.1%
F#/Gb1955
8.0%
G#/Ab1783
7.3%
A#/Bb1733
7.1%
Other values (2)2366
9.6%

Length

2025-12-02T14:39:38.747867image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
c#/db2938
12.0%
c2604
10.6%
g2542
10.4%
a2301
9.4%
b2191
8.9%
d2142
8.7%
f1977
8.1%
f#/gb1955
8.0%
g#/ab1783
7.3%
a#/bb1733
7.1%
Other values (2)2366
9.6%

Most occurring characters

ValueCountFrequency (%)
#9107
14.9%
/9107
14.9%
b9107
14.9%
G6280
10.3%
A5817
9.5%
D5778
9.5%
C5542
9.1%
F3932
6.5%
B3924
6.4%
E2366
 
3.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)60960
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
#9107
14.9%
/9107
14.9%
b9107
14.9%
G6280
10.3%
A5817
9.5%
D5778
9.5%
C5542
9.1%
F3932
6.5%
B3924
6.4%
E2366
 
3.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)60960
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
#9107
14.9%
/9107
14.9%
b9107
14.9%
G6280
10.3%
A5817
9.5%
D5778
9.5%
C5542
9.1%
F3932
6.5%
B3924
6.4%
E2366
 
3.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)60960
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
#9107
14.9%
/9107
14.9%
b9107
14.9%
G6280
10.3%
A5817
9.5%
D5778
9.5%
C5542
9.1%
F3932
6.5%
B3924
6.4%
E2366
 
3.9%

duration_min
Real number (ℝ)

High correlation 

Distinct18175
Distinct (%)74.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.743582
Minimum0.066666667
Maximum8.61875
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size191.8 KiB
2025-12-02T14:39:38.861816image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.066666667
5-th percentile2.4063975
Q13.1055875
median3.5906667
Q34.214425
95-th percentile5.6125517
Maximum8.61875
Range8.5520833
Interquartile range (IQR)1.1088375

Descriptive statistics

Standard deviation1.0109732
Coefficient of variation (CV)0.27005505
Kurtosis2.5772989
Mean3.743582
Median Absolute Deviation (MAD)0.53824167
Skewness1.0965743
Sum91837.554
Variance1.0220668
MonotonicityNot monotonic
2025-12-02T14:39:38.977310image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
430
 
0.1%
3.229
 
0.1%
3.528
 
0.1%
2.66666666720
 
0.1%
319
 
0.1%
3.2518
 
0.1%
3.616
 
0.1%
3.7516
 
0.1%
2.815
 
0.1%
2.515
 
0.1%
Other values (18165)24326
99.2%
ValueCountFrequency (%)
0.066666666671
< 0.1%
0.491551
< 0.1%
0.52381666671
< 0.1%
0.531251
< 0.1%
0.56252
< 0.1%
0.5651
< 0.1%
0.5761
< 0.1%
0.6251
< 0.1%
0.62733333331
< 0.1%
0.68751
< 0.1%
ValueCountFrequency (%)
8.618751
< 0.1%
8.6148833331
< 0.1%
8.6126666671
< 0.1%
8.5993333331
< 0.1%
8.595051
< 0.1%
8.5946666671
< 0.1%
8.5381
< 0.1%
8.5233333331
< 0.1%
8.515551
< 0.1%
8.5053333331
< 0.1%

duration_sec
Real number (ℝ)

High correlation 

Distinct18175
Distinct (%)74.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean224.61492
Minimum4
Maximum517.125
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size191.8 KiB
2025-12-02T14:39:39.090469image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile144.38385
Q1186.33525
median215.44
Q3252.8655
95-th percentile336.7531
Maximum517.125
Range513.125
Interquartile range (IQR)66.53025

Descriptive statistics

Standard deviation60.658393
Coefficient of variation (CV)0.27005505
Kurtosis2.5772989
Mean224.61492
Median Absolute Deviation (MAD)32.2945
Skewness1.0965743
Sum5510253.2
Variance3679.4406
MonotonicityNot monotonic
2025-12-02T14:39:39.688305image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24030
 
0.1%
19229
 
0.1%
21028
 
0.1%
16020
 
0.1%
18019
 
0.1%
19518
 
0.1%
21616
 
0.1%
22516
 
0.1%
16815
 
0.1%
15015
 
0.1%
Other values (18165)24326
99.2%
ValueCountFrequency (%)
41
< 0.1%
29.4931
< 0.1%
31.4291
< 0.1%
31.8751
< 0.1%
33.752
< 0.1%
33.91
< 0.1%
34.561
< 0.1%
37.51
< 0.1%
37.641
< 0.1%
41.251
< 0.1%
ValueCountFrequency (%)
517.1251
< 0.1%
516.8931
< 0.1%
516.761
< 0.1%
515.961
< 0.1%
515.7031
< 0.1%
515.681
< 0.1%
512.281
< 0.1%
511.41
< 0.1%
510.9331
< 0.1%
510.321
< 0.1%

Interactions

2025-12-02T14:39:29.991458image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:03.903242image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:05.288386image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:06.758856image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:08.100136image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:09.624839image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:11.738988image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:13.395094image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:14.842304image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:16.510167image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:18.271985image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:19.790540image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:21.258363image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:23.114476image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:24.652793image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:26.548182image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:28.531693image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:30.083313image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:03.993323image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:05.369624image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:06.837168image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:08.177950image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:09.751189image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:11.823357image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:13.482106image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:14.921559image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:16.600053image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:18.355731image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:19.878928image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:21.350981image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:23.202592image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:24.737710image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:26.691044image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:28.617928image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:30.173236image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:04.098352image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:05.448081image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:06.914515image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:08.413071image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:09.880154image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:11.907346image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:13.563317image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:15.004584image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:16.685808image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:18.452137image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:19.965580image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:21.442977image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:23.293798image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:24.825415image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:26.831177image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:28.704428image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:30.263052image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:04.176818image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:05.526199image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:06.990560image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:08.499892image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:10.010370image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:11.987940image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:13.650218image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:15.093374image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:16.770241image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:18.551676image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:20.051092image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:21.533409image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:23.385594image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:24.910559image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:26.972962image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:28.789075image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:30.354933image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:04.253597image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:05.607925image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:07.067963image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:08.574013image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:10.139003image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:12.070365image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:13.735789image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:15.175644image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:16.852712image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:18.637190image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:20.136666image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:21.619878image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:23.478242image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:24.996848image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:27.109661image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:28.872078image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:30.853609image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:04.326936image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:05.814151image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:07.142736image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:08.649979image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:10.261456image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:12.159171image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:13.815395image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:15.261299image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:16.936310image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:18.724218image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:20.221809image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:21.705208image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:23.565681image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:25.080052image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:27.235842image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:28.955711image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:30.940552image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:04.401868image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:05.888802image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:07.216962image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:08.722921image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:10.382663image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:12.240163image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:13.897847image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:15.374993image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:17.307613image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:18.810919image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:20.304591image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:21.789579image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:23.652630image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:25.168277image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:27.365674image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:29.038432image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:31.026674image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:04.483469image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:05.967701image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:07.294349image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:08.800072image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:10.511429image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:12.326014image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:13.982862image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:15.469493image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:17.404716image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:18.899862image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:20.392167image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:21.879626image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:23.744591image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:25.257666image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:27.495597image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:29.123050image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:31.120094image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:04.562444image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:06.051153image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:07.371100image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:08.878293image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:10.640810image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:12.406037image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:14.068997image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:15.582420image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:17.500187image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:18.988850image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:20.480920image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:21.966973image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:23.835544image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:25.349658image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:27.634448image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:29.208318image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:31.207720image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:04.650312image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:06.128364image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:07.447777image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:08.956939image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:10.774331image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:12.694067image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:14.155747image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:15.688573image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:17.590765image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:19.074910image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:20.567956image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:22.057003image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:23.924548image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:25.439638image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:27.766592image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:29.292469image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:31.295763image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:04.733702image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:06.205573image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:07.524887image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:09.034300image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:10.901134image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:12.770945image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:14.241740image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:15.810266image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:17.687513image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:19.160697image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:20.654274image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:22.148528image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:24.013640image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:25.565437image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:27.889940image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:29.378398image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:31.384702image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:04.814944image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:06.283010image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:07.601393image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:09.110784image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:11.026412image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:12.847138image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:14.326342image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:15.926525image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:17.772586image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:19.246773image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:20.738600image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:22.237209image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:24.103661image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:25.701092image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:27.988297image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:29.465349image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:31.475157image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:04.893218image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:06.365427image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:07.683315image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:09.188208image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:11.148151image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:12.947404image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:14.413078image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:16.028727image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:17.856257image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:19.336791image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:20.825980image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:22.325752image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:24.196316image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:25.846259image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:28.079464image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:29.557107image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:31.567497image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:04.976295image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:06.447129image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:07.767922image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:09.268205image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:11.282643image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:13.058769image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:14.507617image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:16.134462image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:17.940830image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:19.431333image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:20.915867image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:22.419646image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:24.290731image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:25.995285image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:28.172322image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:29.649597image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:31.655220image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:05.056656image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:06.524645image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:07.845026image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:09.345098image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:11.407986image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:13.146526image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:14.595174image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:16.245315image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:18.025046image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:19.523596image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:21.002578image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:22.509609image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:24.380886image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:26.126476image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:28.261468image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:29.734418image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:31.739080image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:05.135408image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:06.605751image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:07.942491image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:09.424540image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:11.537794image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:13.238277image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:14.684806image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:16.329951image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:18.109277image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:19.616489image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:21.090884image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:22.940039image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:24.473436image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:26.276825image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:28.354461image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:29.825939image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:31.820751image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:05.209471image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:06.679488image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:08.017161image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:09.499542image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:11.644340image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:13.315862image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:14.764913image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:16.408721image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:18.186237image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:19.700615image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:21.172670image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:23.024264image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:24.560311image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:26.408740image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:28.441413image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-02T14:39:29.906144image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2025-12-02T14:39:39.780689image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
acousticnessdanceabilityduration_minduration_msduration_secenergyinstrumentalnesskeykey_namelivenessloudnessmoderelease_dayrelease_monthrelease_yearspeechinesstempotrack_popularityvalence
acousticness1.0000.093-0.077-0.077-0.077-0.511-0.1900.0060.025-0.059-0.3120.0350.0160.0240.0570.019-0.1600.1140.071
danceability0.0931.000-0.099-0.099-0.099-0.125-0.0460.0090.036-0.142-0.0310.0570.0260.0310.1200.268-0.1550.0480.334
duration_min-0.077-0.0991.0001.0001.000-0.0070.0590.0150.019-0.039-0.1230.025-0.068-0.093-0.448-0.144-0.019-0.098-0.008
duration_ms-0.077-0.0991.0001.0001.000-0.0070.0590.0150.019-0.039-0.1230.025-0.068-0.093-0.448-0.144-0.019-0.098-0.008
duration_sec-0.077-0.0991.0001.0001.000-0.0070.0590.0150.019-0.039-0.1230.025-0.068-0.093-0.448-0.144-0.019-0.098-0.008
energy-0.511-0.125-0.007-0.007-0.0071.0000.0920.0110.0170.1480.6670.028-0.022-0.029-0.0120.0790.174-0.1040.138
instrumentalness-0.190-0.0460.0590.0590.0590.0921.0000.0180.007-0.035-0.1760.0170.0200.019-0.002-0.2050.059-0.167-0.153
key0.0060.0090.0150.0150.0150.0110.0181.0001.000-0.001-0.0040.307-0.0030.002-0.0080.024-0.010-0.0080.021
key_name0.0250.0360.0190.0190.0190.0170.0071.0001.0000.0180.0190.3090.0060.0030.0400.0350.0160.0150.017
liveness-0.059-0.142-0.039-0.039-0.0390.148-0.035-0.0010.0181.0000.0950.0030.001-0.0100.0020.0620.026-0.032-0.049
loudness-0.312-0.031-0.123-0.123-0.1230.667-0.176-0.0040.0190.0951.0000.0000.0060.0000.1740.1040.1130.0540.045
mode0.0350.0570.0250.0250.0250.0280.0170.3070.3090.0030.0001.0000.0260.0170.0850.0640.0260.0270.000
release_day0.0160.026-0.068-0.068-0.068-0.0220.020-0.0030.0060.0010.0060.0261.0000.2180.1590.0290.0020.021-0.057
release_month0.0240.031-0.093-0.093-0.093-0.0290.0190.0020.003-0.0100.0000.0170.2181.0000.1580.0180.0110.045-0.058
release_year0.0570.120-0.448-0.448-0.448-0.012-0.002-0.0080.0400.0020.1740.0850.1590.1581.0000.1510.0470.111-0.205
speechiness0.0190.268-0.144-0.144-0.1440.079-0.2050.0240.0350.0620.1040.0640.0290.0180.1511.0000.020-0.0050.079
tempo-0.160-0.155-0.019-0.019-0.0190.1740.059-0.0100.0160.0260.1130.0260.0020.0110.0470.0201.000-0.006-0.049
track_popularity0.1140.048-0.098-0.098-0.098-0.104-0.167-0.0080.015-0.0320.0540.0270.0210.0450.111-0.005-0.0061.0000.033
valence0.0710.334-0.008-0.008-0.0080.138-0.1530.0210.017-0.0490.0450.000-0.057-0.058-0.2050.079-0.0490.0331.000

Missing values

2025-12-02T14:39:31.956614image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2025-12-02T14:39:32.251922image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

track_nametrack_artisttrack_popularitytrack_album_nametrack_album_release_datedanceabilityenergykeyloudnessmodespeechinessacousticnessinstrumentalnesslivenessvalencetempoduration_msrelease_yearrelease_monthrelease_daykey_nameduration_minduration_sec
0Dance MonkeyTones and I100Dance Monkey (Stripped Back) / Dance Monkey2019-10-170.8240.5886-6.40000.09240.692000.0001040.14900.51398.02720943820191017F#/Gb3.490633209.438
1ROXANNEArizona Zervas99ROXANNE2019-10-100.6210.6016-5.61600.14800.052200.0000000.46000.457116.73516363620191010F#/Gb2.727267163.636
2Blinding LightsThe Weeknd98Blinding Lights2019-11-290.5130.7961-4.07510.06290.001470.0002090.09380.345171.01720157320191129C#/Db3.359550201.573
3CirclesPost Malone98Hollywood's Bleeding2019-09-060.6950.7620-3.49710.03950.192000.0024400.08630.553120.042215280201996C3.588000215.280
4TusaKAROL G98Tusa2019-11-070.8030.7152-3.28010.29800.295000.0001340.05740.574101.0852009602019117D3.349333200.960
5The BoxRoddy Ricch98Please Excuse Me For Being Antisocial2019-12-060.8960.58610-6.68700.05590.104000.0000000.79000.642116.9711966532019126A#/Bb3.277550196.653
6MemoriesMaroon 598Memories2019-09-200.7640.32011-7.20910.05460.837000.0000000.08220.57591.0191894862019920B3.158100189.486
7FallingTrevor Daniel97Falling2018-10-050.7840.43010-8.75600.03640.123000.0000000.08870.236127.0871593822018105A#/Bb2.656367159.382
8everything i wantedBillie Eilish97everything i wanted2019-11-130.7040.2256-14.45400.09940.902000.6570000.10600.243120.00624542620191113F#/Gb4.090433245.426
9Don't Start NowDua Lipa97Don't Start Now2019-10-310.7940.79311-4.52100.08420.012500.0000000.09520.677123.94118329020191031B3.054833183.290
track_nametrack_artisttrack_popularitytrack_album_nametrack_album_release_datedanceabilityenergykeyloudnessmodespeechinessacousticnessinstrumentalnesslivenessvalencetempoduration_msrelease_yearrelease_monthrelease_daykey_nameduration_minduration_sec
24522Limitless - Radio MixBURNS0Limitless2013-07-050.5450.9010-2.68310.08010.009450.0000030.27000.302125.069225867201375C3.764450225.867
24523Reverence - Original MixNorthend0Reverence2013-04-010.6420.7356-7.71000.07010.004080.4410000.37900.292127.979364000201341F#/Gb6.066667364.000
24524Feel Again - Thomas Gold Club MixOneRepublic0Native (Deluxe)2013-01-010.6550.94910-4.97910.05260.002130.0727000.05420.376128.015406040201311A#/Bb6.767333406.040
24525StarsUmmet Ozcan0Stars2015-09-070.6200.8874-2.46400.04440.105000.0009160.32200.308128.102187505201597E3.125083187.505
24526We Are The Ones - Radio Edittwoloud0We Are The Ones2014-09-290.4990.8478-4.80300.11600.011200.0000000.48600.257128.1142092432014929G#/Ab3.487383209.243
24527What Does Tomorrow Bring (Extended Mix) [feat. Natalie Peris]Richard Beynon0What Does Tomorrow Bring2012-10-160.6820.97111-4.36200.19800.140000.2420000.88300.286127.99336562520121016B6.093750365.625
24528I Surrender (Extended Mix)CAZZETTE0Eject pt. II2012-12-120.7510.5774-5.07300.06770.005990.0027800.08400.187128.00139047920121212E6.507983390.479
24529Every Day - Radio EditEric Prydz0Every Day2012-01-060.4500.8707-4.78400.04690.000400.0734000.43400.164125.970175520201216G2.925333175.520
24530Heiress of Valentina - Alesso Exclusive MixDune0Best of Club Session 20122012-11-210.6020.9289-4.85010.10500.019400.0013100.71700.220128.02238400020121121A6.400000384.000
24531Jupiter Unison - 3LAU Bootleg3LAU0Dance Floor Filth 22012-07-170.6150.7516-5.35500.05450.019500.0000000.30900.574127.3892045652012717F#/Gb3.409417204.565